Labelbox Bets on Experts Even as AI Leans Into Self-Training

"Nearly every dataset... requires a fusion of AI software and humans. There is just no way to produce the best data in isolation.”
As AI companies scramble to build ever more capable models, data labeling has gotten its moment in the spotlight. Ex. the Meta-Scale AI deal, which saw Meta take a 49% stake in Scale and hire its CEO, injected new energy and scrutiny. With foundational models shifting from static, pre-trained systems to continually evolving, reinforcement-tuned AI agents, the role of high-quality, domain-specific data is more important than ever.  Labelbox wants to lead this shift, even as advances in synthetic training models raise existential questions about the future of human-in-the-loop data labeling.
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Picture of Mukundan Sivaraj
Mukundan Sivaraj
Mukundan is a writer and editor covering the AI startup ecosystem at AIM Media House. Reach out to him at mukundan.sivaraj@analyticsindiamag.com.
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